Bike Store Sales Data Analysis¶

Import required modules¶

In [1]:
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
import seaborn as sns
import plotly.express as px
import numpy as np
%matplotlib inline

Read sales data from a csv file¶

In [2]:
sales_df = pd.read_csv('sales_data.csv', parse_dates=['Date'])

The data at a glance¶

In [3]:
sales_df.head()
Out[3]:
Date Day Month Year Customer_Age Age_Group Customer_Gender Country State Product_Category Sub_Category Product Order_Quantity Unit_Cost Unit_Price Profit Cost Revenue
0 2013-11-26 26 November 2013 19 Youth (<25) M Canada British Columbia Accessories Bike Racks Hitch Rack - 4-Bike 8 45 120 590 360 950
1 2015-11-26 26 November 2015 19 Youth (<25) M Canada British Columbia Accessories Bike Racks Hitch Rack - 4-Bike 8 45 120 590 360 950
2 2014-03-23 23 March 2014 49 Adults (35-64) M Australia New South Wales Accessories Bike Racks Hitch Rack - 4-Bike 23 45 120 1366 1035 2401
3 2016-03-23 23 March 2016 49 Adults (35-64) M Australia New South Wales Accessories Bike Racks Hitch Rack - 4-Bike 20 45 120 1188 900 2088
4 2014-05-15 15 May 2014 47 Adults (35-64) F Australia New South Wales Accessories Bike Racks Hitch Rack - 4-Bike 4 45 120 238 180 418

Number of rows and columns¶

In [4]:
sales_df.shape
Out[4]:
(113036, 18)
113036 rows of data and 18 columns¶

Some information about data in columns¶

In [5]:
sales_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 113036 entries, 0 to 113035
Data columns (total 18 columns):
 #   Column            Non-Null Count   Dtype         
---  ------            --------------   -----         
 0   Date              113036 non-null  datetime64[ns]
 1   Day               113036 non-null  int64         
 2   Month             113036 non-null  object        
 3   Year              113036 non-null  int64         
 4   Customer_Age      113036 non-null  int64         
 5   Age_Group         113036 non-null  object        
 6   Customer_Gender   113036 non-null  object        
 7   Country           113036 non-null  object        
 8   State             113036 non-null  object        
 9   Product_Category  113036 non-null  object        
 10  Sub_Category      113036 non-null  object        
 11  Product           113036 non-null  object        
 12  Order_Quantity    113036 non-null  int64         
 13  Unit_Cost         113036 non-null  int64         
 14  Unit_Price        113036 non-null  int64         
 15  Profit            113036 non-null  int64         
 16  Cost              113036 non-null  int64         
 17  Revenue           113036 non-null  int64         
dtypes: datetime64[ns](1), int64(9), object(8)
memory usage: 15.5+ MB

Missing values for each column¶

In [6]:
sales_df.isnull().sum()
Out[6]:
Date                0
Day                 0
Month               0
Year                0
Customer_Age        0
Age_Group           0
Customer_Gender     0
Country             0
State               0
Product_Category    0
Sub_Category        0
Product             0
Order_Quantity      0
Unit_Cost           0
Unit_Price          0
Profit              0
Cost                0
Revenue             0
dtype: int64
No missing values¶

Descriptive Statistics of Numerical columns¶

In [7]:
sales_df.describe()
Out[7]:
Date Day Year Customer_Age Order_Quantity Unit_Cost Unit_Price Profit Cost Revenue
count 113036 113036.000000 113036.000000 113036.000000 113036.000000 113036.000000 113036.000000 113036.000000 113036.000000 113036.000000
mean 2014-11-23 12:14:55.063519232 15.665753 2014.401739 35.919212 11.901660 267.296366 452.938427 285.051665 469.318695 754.370360
min 2011-01-01 00:00:00 1.000000 2011.000000 17.000000 1.000000 1.000000 2.000000 -30.000000 1.000000 2.000000
25% 2013-12-22 00:00:00 8.000000 2013.000000 28.000000 2.000000 2.000000 5.000000 29.000000 28.000000 63.000000
50% 2014-06-27 00:00:00 16.000000 2014.000000 35.000000 10.000000 9.000000 24.000000 101.000000 108.000000 223.000000
75% 2016-01-09 00:00:00 23.000000 2016.000000 43.000000 20.000000 42.000000 70.000000 358.000000 432.000000 800.000000
max 2016-07-31 00:00:00 31.000000 2016.000000 87.000000 32.000000 2171.000000 3578.000000 15096.000000 42978.000000 58074.000000
std NaN 8.781567 1.272510 11.021936 9.561857 549.835483 922.071219 453.887443 884.866118 1309.094674

The ranges of values in numerical columns seem reasonable.¶

Data is for the period between 2011 and 2016¶
Customer Age is in the range of 17 to 87¶
Average number of order quantity = 11.9, Maximum = 32¶
Minimum Unit Cost = 1 and Maximum Unit Cost = 2171¶
Minimum Unit Price = 2 and Maximum Unit Price = 3578¶
Average Profit = 285, Minimum Profit = -30 ? and Maximum Profit = 15096¶
Average Revenue = 754.3, Maximum Revenue = 58074¶

Box Plots for Numerical Columns¶

In [8]:
boxplot_cols = ['Year', 'Customer_Age', 'Order_Quantity', 'Unit_Cost', 'Unit_Price', 'Profit']

sales_df[boxplot_cols].plot(kind='box', subplots=True, layout=(2,3), figsize=(14,8))
Out[8]:
Year                 Axes(0.125,0.53;0.227941x0.35)
Customer_Age      Axes(0.398529,0.53;0.227941x0.35)
Order_Quantity    Axes(0.672059,0.53;0.227941x0.35)
Unit_Cost            Axes(0.125,0.11;0.227941x0.35)
Unit_Price        Axes(0.398529,0.11;0.227941x0.35)
Profit            Axes(0.672059,0.11;0.227941x0.35)
dtype: object
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Relationship between Numerical Columns¶

In [9]:
numerical_df = sales_df.select_dtypes(include=[float, int])
corr = numerical_df.corr()
corr
Out[9]:
Day Year Customer_Age Order_Quantity Unit_Cost Unit_Price Profit Cost Revenue
Day 1.000000 -0.007635 -0.014296 -0.002412 0.003133 0.003207 0.004623 0.003329 0.003853
Year -0.007635 1.000000 0.040994 0.123169 -0.217575 -0.213673 -0.181525 -0.215604 -0.208673
Customer_Age -0.014296 0.040994 1.000000 0.026887 -0.021374 -0.020262 0.004319 -0.016013 -0.009326
Order_Quantity -0.002412 0.123169 0.026887 1.000000 -0.515835 -0.515925 -0.238863 -0.340382 -0.312895
Unit_Cost 0.003133 -0.217575 -0.021374 -0.515835 1.000000 0.997894 0.741020 0.829869 0.817865
Unit_Price 0.003207 -0.213673 -0.020262 -0.515925 0.997894 1.000000 0.749870 0.826301 0.818522
Profit 0.004623 -0.181525 0.004319 -0.238863 0.741020 0.749870 1.000000 0.902233 0.956572
Cost 0.003329 -0.215604 -0.016013 -0.340382 0.829869 0.826301 0.902233 1.000000 0.988758
Revenue 0.003853 -0.208673 -0.009326 -0.312895 0.817865 0.818522 0.956572 0.988758 1.000000
In [10]:
fig = plt.figure(figsize=(10,8))
plt.matshow(corr, cmap='RdBu', fignum=fig.number)
plt.colorbar()
plt.xticks(range(len(corr.columns)), corr.columns, rotation='vertical');
plt.yticks(range(len(corr.columns)), corr.columns);
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Scatter Plot between Revenue and Order Quantity, Unit Cost, Unit Price, Profit¶

In [11]:
fig = plt.figure(figsize=(18,10))
ax0 = fig.add_subplot(2,2,1)
ax1 = fig.add_subplot(2,2,2)
ax2 = fig.add_subplot(2,2,3)
ax3 = fig.add_subplot(2,2,4)
sales_df[['Order_Quantity', 'Revenue']].plot(kind='scatter', x='Order_Quantity', y='Revenue', ax=ax0)
ax0.set_xlabel('Order Quantity', fontsize=12)
ax0.set_ylabel('Revenue', fontsize=12)
ax0.set_title('Revenue vs Order Quantity', fontsize=14)
sales_df[['Unit_Cost', 'Revenue']].plot(kind='scatter', x='Unit_Cost', y='Revenue', ax=ax1)
ax1.set_xlabel('Unit Cost', fontsize=12)
ax1.set_ylabel('Revenue', fontsize=12)
ax1.set_title('Revenue vs Unit Cost', fontsize=14)
sales_df[['Unit_Price', 'Revenue']].plot(kind='scatter', x='Unit_Price', y='Revenue', ax=ax2)
ax2.set_xlabel('Unit Price', fontsize=12)
ax2.set_ylabel('Revenue', fontsize=12)
ax2.set_title('Revenue vs Unit Price', fontsize=14)
sales_df[['Profit', 'Revenue']].plot(kind='scatter', x='Profit', y='Revenue', ax=ax3)
ax3.set_xlabel('Profit', fontsize=12)
ax3.set_ylabel('Revenue', fontsize=12)
ax3.set_title('Revenue vs Profit', fontsize=14)
plt.tight_layout()
plt.show()
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Products with Highest Unit Cost¶

In [12]:
max_unit_cost = sales_df.loc[sales_df['Unit_Cost'] == sales_df['Unit_Cost'].max(), ['Product_Category', 'Sub_Category', 'Product', 'Unit_Cost', 'Unit_Price']].drop_duplicates()
max_unit_cost.reset_index(drop=True)
Out[12]:
Product_Category Sub_Category Product Unit_Cost Unit_Price
0 Bikes Road Bikes Road-150 Red, 48 2171 3578
1 Bikes Road Bikes Road-150 Red, 62 2171 3578
2 Bikes Road Bikes Road-150 Red, 52 2171 3578
3 Bikes Road Bikes Road-150 Red, 44 2171 3578
4 Bikes Road Bikes Road-150 Red, 56 2171 3578
Products with highest Unit Cost are the same with highest Unit Price¶

Order with profit = -30¶

In [13]:
sales_df[sales_df['Profit'] == sales_df['Profit'].min()]
Out[13]:
Date Day Month Year Customer_Age Age_Group Customer_Gender Country State Product_Category Sub_Category Product Order_Quantity Unit_Cost Unit_Price Profit Cost Revenue
48571 2015-12-17 17 December 2015 27 Young Adults (25-34) F France Yveline Clothing Jerseys Short-Sleeve Classic Jersey, XL 31 42 54 -30 1302 1272

Products Category¶

In [14]:
product_category = sales_df['Product_Category'].unique()
product_category
Out[14]:
array(['Accessories', 'Clothing', 'Bikes'], dtype=object)
There are three main product categories: Accessories, Clothing and Bikes¶

When different products were added to Bike Store¶

In [15]:
sales_df.groupby('Year')['Product_Category'].unique().reset_index()
Out[15]:
Year Product_Category
0 2011 [Bikes]
1 2012 [Bikes]
2 2013 [Accessories, Clothing, Bikes]
3 2014 [Accessories, Clothing, Bikes]
4 2015 [Accessories, Clothing, Bikes]
5 2016 [Accessories, Clothing, Bikes]
Accessories and Clothing were added from 2013¶

Countries Products are sold in:¶

In [16]:
countries = sales_df['Country'].unique()
countries
Out[16]:
array(['Canada', 'Australia', 'United States', 'Germany', 'France',
       'United Kingdom'], dtype=object)
Products are sold in: Canada, Australia, United States, Germany, France and United Kingdom¶

Number of Unique Products for each Product category and Sub category¶

In [17]:
category_info = sales_df.groupby('Product_Category').agg(
    Sub_Category=('Sub_Category', 'nunique'),
    Products=('Product', 'nunique')
).reset_index()
category_info
Out[17]:
Product_Category Sub_Category Products
0 Accessories 8 23
1 Bikes 3 88
2 Clothing 6 23

Check for inaccurate entries in Cost and Revenue columns¶

Add and Calculate a new Calculated_Cost column¶

Used formula

$$ Calculated\_Cost = Order\_Quantity * Unit\_Cost $$
In [18]:
sales_df['Calculated_Cost'] = sales_df['Order_Quantity'] * sales_df['Unit_Cost']
(sales_df['Calculated_Cost'] != sales_df['Cost']).sum()
Out[18]:
0
There is nothing wrong with Cost cloumn entries.¶

Add and calculate a new Calculated_Revenue column¶

Used formula

$$ Calculated\_Revenue = Cost + Profit $$
In [19]:
sales_df['Calculated_Revenue'] = sales_df['Cost'] + sales_df['Profit']
(sales_df['Calculated_Revenue'] != sales_df['Revenue']).sum()
Out[19]:
0
There is nothing wrong with Revenue cloumn entries.¶

Explortory Analysis and Visualization¶

Distribution of Order Quantity¶

In [20]:
sales_df['Order_Quantity'].plot(kind='hist', edgecolor='black', bins=31)
plt.title('Distribution of Order Quantities')
plt.xlabel('Order Quantity')
plt.ylabel('Frequency')
plt.show()
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In [21]:
# Function to format the y-ticks in thousands
def thousands_formatter(x, pos):
    return '%1.0fK' % (x * 1e-3)

Order Quantity by Product Category¶

In [22]:
orders = sales_df.groupby('Product_Category')[['Order_Quantity']].sum().reset_index()
print(orders)
ax = sns.barplot(data=orders, x='Product_Category', y='Order_Quantity')
plt.title('Total Order Quantity by Product Category')
ax.ticklabel_format(style='plain', axis='y')
ax.yaxis.set_major_formatter(FuncFormatter(thousands_formatter))
plt.xlabel('Product Category')
plt.ylabel('Total Order Quantity')
plt.show()

  Product_Category  Order_Quantity
0      Accessories         1054162
1            Bikes           36411
2         Clothing          254743
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Revenue by Year¶

In [23]:
revenue = sales_df.groupby('Year')['Revenue'].sum()
print(revenue.reset_index())
ax = revenue.plot(marker='o')
ax.yaxis.set_major_formatter(FuncFormatter(thousands_formatter))
plt.xlabel('Year', fontsize=12)
plt.ylabel('Revenue', fontsize=12)
plt.title('Total Revenue by Year', fontsize=14)
plt.grid(True)
plt.show()

   Year   Revenue
0  2011   8964888
1  2012   9175983
2  2013  15240037
3  2014  14152724
4  2015  20023991
5  2016  17713385
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Revenue by month¶

In [24]:
revenue_month = sales_df.groupby('Month')['Revenue'].sum().reset_index()
month_order = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']
revenue_month['Month'] = pd.Categorical(revenue_month['Month'], categories=month_order, ordered=True)
revenue_month.sort_values(by='Month', inplace=True)
fig = plt.figure(figsize=(12,6))
ax = sns.barplot(data=revenue_month, x='Month', y='Revenue')
ax.yaxis.set_major_formatter(FuncFormatter(thousands_formatter))
plt.ylabel('Revenue', fontsize=12)
plt.xlabel('Month', fontsize=12)
plt.title('Revenue by Month', fontsize=14)
plt.grid(True)
plt.show()

C:\Users\deepa\anaconda3\Lib\site-packages\seaborn\categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped_vals = vals.groupby(grouper)
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How did revenue and sales evolve through the years?¶

Revenue¶

In [25]:
sales_df.groupby('Date')['Revenue'].sum().plot(kind='line', figsize=(14,6))
plt.xlabel('Year', fontsize=12)
plt.ylabel('Revenue', fontsize=12)
plt.title('Revenue per Day', fontsize=14)
plt.grid(True)
plt.show()
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Sales¶

In [26]:
sales_df['Date'].value_counts().plot(kind='line', figsize=(14,6))
plt.xlabel('Year', fontsize=12)
plt.ylabel('Number of Sales', fontsize=12)
plt.title('Number of Sales per Day', fontsize=14)
plt.grid(True)
plt.show()
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Revenue by Product Category, Sub Category and Product¶

In [27]:
# Sunburst plot for sales revenue
fig = px.sunburst(sales_df, path=['Product_Category', 'Sub_Category', 'Product'], values='Revenue', title='Revenue for Sales')

fig.update_layout(width=1000, height=800) 
fig.show()

Revenue by Product Category¶

In [28]:
revenue_cat = sales_df.groupby('Product_Category')['Revenue'].sum().reset_index()
print(revenue_cat)
ax = sns.barplot(data=revenue_cat, x='Product_Category', y='Revenue')
plt.title('Total Revenue by Product Category')
ax.ticklabel_format(style='plain', axis='y')
ax.yaxis.set_major_formatter(FuncFormatter(thousands_formatter))
plt.xlabel('Product Category')
plt.ylabel('Total Revenue')
plt.show()

  Product_Category   Revenue
0      Accessories  15117992
1            Bikes  61782134
2         Clothing   8370882
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In [29]:
fig = plt.figure(figsize=(10,8))
sns.set_style("whitegrid")
ax0 = fig.add_subplot(1, 2, 1)
ax1 = fig.add_subplot(1, 2, 2)
ax0.pie(orders['Order_Quantity'], labels=orders['Product_Category'], autopct='%1.1f%%')
ax0.set_title('Order Quantity by Product Category')
ax0.set_ylabel('')
ax1.pie(revenue_cat['Revenue'], labels=revenue_cat['Product_Category'], autopct='%1.1f%%')
ax1.set_title('Revenue by Product Category')
ax1.set_ylabel('')
plt.show()
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Based on Pie Charts: Even though the number of Bikes ordered is the lowest it contributes most to Total Revenue¶

Revenue for Product Category by Year¶

In [30]:
revenue_year = sales_df.pivot_table(index='Year', columns='Product_Category', values='Revenue', aggfunc='sum', fill_value=0)
revenue_year
Out[30]:
Product_Category Accessories Bikes Clothing
Year
2011 0 8964888 0
2012 0 9175983 0
2013 3384215 9858787 1997035
2014 4293592 7611243 2247889
2015 3285954 14799083 1938954
2016 4154231 11372150 2187004
In [31]:
ax =revenue_year.plot(kind='bar', stacked=True, figsize=(12, 8))

# Add labels and title
plt.xlabel('Year', fontsize=12)
plt.ylabel('Revenue', fontsize=12)
plt.title('Revenue by Product Category and Year')
ax.ticklabel_format(style='plain', axis='y')
ax.yaxis.set_major_formatter(FuncFormatter(thousands_formatter))
plt.legend(title='Product Category', loc='upper left', fontsize=12)

# Display the plot
plt.show()
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Top 10 Ordered Products¶

In [32]:
orders_top10 = sales_df.groupby(['Product_Category', 'Sub_Category', 'Product'])['Order_Quantity'].sum()
orders_top10.sort_values(inplace=True)
orders_top10 = orders_top10.tail(10)
print(orders_top10.sort_values(ascending=False).reset_index())
ax = orders_top10.plot(kind='barh', color=['Purple', 'Grey'])
plt.xlabel('Order Quantity', fontsize=12)
plt.ylabel('Product', fontsize=12)
ax.ticklabel_format(style='plain', axis='x')
ax.xaxis.set_major_formatter(FuncFormatter(thousands_formatter))
plt.title('Top 10 Products by Order Quantity')
plt.show()

  Product_Category       Sub_Category                  Product  Order_Quantity
0      Accessories  Bottles and Cages    Water Bottle - 30 oz.          164082
1      Accessories    Tires and Tubes      Patch Kit/8 Patches          157583
2      Accessories    Tires and Tubes       Mountain Tire Tube          102792
3         Clothing               Caps             AWC Logo Cap           67264
4      Accessories            Helmets    Sport-100 Helmet, Red           63663
5      Accessories    Tires and Tubes           Road Tire Tube           62296
6      Accessories            Fenders    Fender Set - Mountain           62081
7      Accessories            Helmets  Sport-100 Helmet, Black           62048
8      Accessories    Tires and Tubes        Touring Tire Tube           56802
9      Accessories            Helmets   Sport-100 Helmet, Blue           55895
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Top 10 Products by Revenue¶

In [33]:
product_top10 = sales_df.groupby(['Product_Category', 'Sub_Category', 'Product'])['Revenue'].sum()
product_top10.sort_values(inplace=True)
product_top10 = product_top10.tail(10)
print(product_top10.sort_values(ascending=False).reset_index())
ax = product_top10.plot(kind='barh', color=['Purple', 'Grey'])
plt.xlabel('Revenue', fontsize=12)
plt.ylabel('Product', fontsize=12)
ax.ticklabel_format(style='plain', axis='x')
ax.xaxis.set_major_formatter(FuncFormatter(thousands_formatter))
plt.title('Top 10 Products by Revenue')
plt.show()

  Product_Category    Sub_Category                  Product  Revenue
0            Bikes      Road Bikes         Road-150 Red, 62  3829416
1            Bikes  Mountain Bikes   Mountain-200 Black, 38  3366248
2            Bikes      Road Bikes         Road-150 Red, 52  3180840
3            Bikes      Road Bikes         Road-150 Red, 56  3158805
4            Bikes  Mountain Bikes  Mountain-200 Silver, 42  3081078
5            Bikes  Mountain Bikes  Mountain-200 Silver, 38  3035442
6            Bikes      Road Bikes         Road-150 Red, 48  2965309
7            Bikes  Mountain Bikes   Mountain-200 Black, 46  2769620
8            Bikes  Mountain Bikes   Mountain-200 Black, 42  2641606
9            Bikes  Mountain Bikes  Mountain-200 Silver, 46  2376146
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Number of Sales, Order Quantity and Revenue by Customer gender¶

In [34]:
grouped_df = sales_df.groupby('Customer_Gender').agg({
    'Order_Quantity': 'sum',
    'Revenue': 'sum',
    'Customer_Gender': 'count'  # Count of sales per gender
}).rename(columns={'Customer_Gender': 'Number_of_Sales'}).reset_index()
grouped_df.Customer_Gender = ['Female', 'Male']
grouped_df
Out[34]:
Customer_Gender Order_Quantity Revenue Number_of_Sales
0 Female 645733 41935599 54724
1 Male 699583 43335409 58312
In [35]:
fig, axes = plt.subplots(1, 3, figsize=(18, 8))

# Plotting Number of Sales by Gender
sns.barplot(data=grouped_df, x='Customer_Gender', y='Number_of_Sales', ax=axes[0])
axes[0].set_title('Number of Sales by Gender', fontsize=20)
axes[0].set_xlabel('Customer Gender', fontsize=15)
axes[0].set_ylabel('Number of Sales', fontsize=15)
axes[0].tick_params(axis='both', which='major', labelsize=12) 
axes[0].yaxis.set_major_formatter(FuncFormatter(thousands_formatter))

# Plotting Order Quantity by Gender
sns.barplot(data=grouped_df, x='Customer_Gender', y='Order_Quantity', ax=axes[1])
axes[1].set_title('Order Quantity by Gender', fontsize=20)
axes[1].set_xlabel('Customer Gender', fontsize=15)
axes[1].set_ylabel('Order Quantity', fontsize=15)
axes[1].tick_params(axis='both', which='major', labelsize=12)
axes[1].yaxis.set_major_formatter(FuncFormatter(thousands_formatter))

# Plotting Revenue by Gender
sns.barplot(data=grouped_df, x='Customer_Gender', y='Revenue', ax=axes[2])
axes[2].set_title('Revenue by Gender', fontsize=20)
axes[2].set_xlabel('Customer Gender', fontsize=15)
axes[2].set_ylabel('Revenue', fontsize=15)
axes[2].tick_params(axis='both', which='major', labelsize=12) 
axes[2].yaxis.set_major_formatter(FuncFormatter(thousands_formatter))

# Adjust layout
plt.tight_layout()

# Display the plot
plt.show()
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Number of Sales, Order Quantity and Revenue by Age Group¶

In [36]:
age_grouped_df = sales_df.groupby('Age_Group').agg({
    'Order_Quantity': 'sum',
    'Revenue': 'sum',
    'Age_Group': 'count'  # Count of sales per gender
}).rename(columns={'Age_Group': 'Number_of_Sales'}).reset_index()

age_grouped_df
Out[36]:
Age_Group Order_Quantity Revenue Number_of_Sales
0 Adults (35-64) 672417 42584153 55824
1 Seniors (64+) 9877 308042 730
2 Young Adults (25-34) 446875 30655614 38654
3 Youth (<25) 216147 11723199 17828
In [37]:
fig = plt.figure(figsize=(16,10))
sns.set_style("whitegrid")
ax0 = fig.add_subplot(1, 3, 1)
ax1 = fig.add_subplot(1, 3, 2)
ax2 = fig.add_subplot(1, 3, 3)
ax0.pie(age_grouped_df['Number_of_Sales'], labels=age_grouped_df['Age_Group'], autopct='%1.1f%%')
ax0.set_title('Number of Sales by Age Group')
ax0.set_ylabel('')
ax1.pie(age_grouped_df['Order_Quantity'], labels=age_grouped_df['Age_Group'], autopct='%1.1f%%')
ax1.set_title('Order Quantity by Age Group')
ax1.set_ylabel('')
ax2.pie(age_grouped_df['Revenue'], labels=age_grouped_df['Age_Group'], autopct='%1.1f%%')
ax2.set_title('Revenue by Age Group')
ax2.set_ylabel('')
plt.show()
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Customer Age by Country¶

In [38]:
fig, ax = plt.subplots(figsize=(10, 6))
sales_df[['Customer_Age', 'Country']].boxplot(by='Country', ax=ax)
ax.set_title('')
plt.ylabel('Customer Age', fontsize=12)
plt.xlabel('Country', fontsize=12)
plt.title('Box Plot for Customer Age by Country', fontsize=14)
plt.show()
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Revenue by Country¶

In [39]:
revenue_contury = sales_df.groupby('Country')['Revenue'].sum().reset_index()
revenue_contury
Out[39]:
Country Revenue
0 Australia 21302059
1 Canada 7935738
2 France 8432872
3 Germany 8978596
4 United Kingdom 10646196
5 United States 27975547
In [40]:
plt.figure(figsize=(10, 6))
ax = sns.barplot(data=revenue_contury, x='Country', y='Revenue')
ax.yaxis.set_major_formatter(FuncFormatter(thousands_formatter))
plt.ylabel('Revenue', fontsize=12)
plt.xlabel('Country', fontsize=12)
plt.title('Revenue by Country', fontsize=14)
plt.show()
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Top 10 Revenue generating States¶

In [41]:
revenue_state = sales_df.groupby(['Country', 'State'])['Revenue'].sum().reset_index()
revenue_state.sort_values(by='Revenue', ascending=False, inplace=True)
revenue_state_top10 = revenue_state.head(10)
revenue_state_top10
Out[41]:
Country State Revenue
33 United States California 17672620
30 United Kingdom England 10646196
0 Australia New South Wales 9203495
6 Canada British Columbia 7877890
51 United States Washington 6741510
1 Australia Queensland 5066267
4 Australia Victoria 5054839
46 United States Oregon 3450410
27 Germany Hessen 2073391
29 Germany Saarland 2029545
In [42]:
revenue_state_top10.loc[:, 'Country_State'] = revenue_state_top10.loc[:, 'Country'] + '-' + revenue_state_top10.loc[: ,'State']
fig = plt.figure(figsize=(10,6))
ax = sns.barplot(data=revenue_state_top10, x='Revenue', y='Country_State')
ax.xaxis.set_major_formatter(FuncFormatter(thousands_formatter))
plt.ylabel('Country-State', fontsize=12)
plt.xlabel('Revenue', fontsize=12)
plt.title('Top 10 Revenue generating States')
plt.show()

C:\Users\deepa\AppData\Local\Temp\ipykernel_19316\3162127595.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
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